Guide to SQL Server Table Indexes – Part 4

In the first part of this article
series, you learned about the basics of SQL Server database table’s indexes,
the difference between the clustered and non-clustered indexes, and how the
leaf nodes, non-leaf nodes, and heaps associated with data storage. In the
second and third part, you learned about the different types of indexes
available in SQL Server, and what are these indexes advantages and

In this article, you will learn about
index design and columns, candidates for index key columns, index placement, and
pad index and the fill-factor option.

Index design

Well-designed indexes are an
important part of database optimisation because indexes help to reduce query
response time while improving the overall performance of query without
affecting the design of underlying table/tables. However, index design is a
very complex, time consuming and error prone process, even for databases and
workloads with moderate complexity, and during the Index design process, you
make decisions on index key columns, non-key columns, and index storage
locations. You should carefully consider included columns as key columns,
because they depend on how the data is accessed by users of databases and
database applications.

General Guidelines for Index Design

In addition to the primary key and
unique constraint indexes created automatically during table creation, the good
index candidates include:

  • The
    higher the level of selectivity in a column, the more likely it is as a key
    column candidate.
  • Foreign
    keys are good candidates for non-clustered indexes because they are mostly used in join operations.
  • Columns
    used in joining tables are good candidates for non-clustered indexes because it
    will improve query performance significantly in join
    operations by reducing the time required in locating the required rows in each of
    the join tables.
  • Columns
    searched for specific values or ranges of values are good candidates for
    non-clustered index or filtered index.  Filtered indexes are especially useful
    to cover those queries that frequently needs access to well-defined subset of
  • Columns
    used in sort order are good candidates for non-clustered or clustered index.
  • Columns
    queried for data returned organized as groups.
  • Columns
    used in LIKE comparisons.
  • You
    can include computed columns as key columns, but with the following

    • The
      value must be persisted
    • The
      column result must be deterministic (always return the same result).
    • The
      column cannot generate a text, ntext, or image data type value.

SET options are an additional
concern. SET options are connection-specific. When working with computed
columns, you must set the following options in the connection creating the
index, and any connection modifying the column data. If the options are not set
correctly, errors can occur when you create the column or modify key column

SET Option
















Index placement

The physical location of the data
pages that make up the index is referred to as the index placement. Unless you
specify otherwise, the database engine writes any index you create to the
filegroup containing its base table (see below):

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You can specify to create a
clustered or non-clustered index on a different filegroup. When you create a
clustered index, the clustered index and the table are saved in the same
physical location. Rebuilding clustered index on an alternate filegroup also saves
table on the same alternate filegroup.

When you create a non-clustered
index, the storage hierarchy of a non-clustered index is not physically related
to the table on which it is based. You can store a table and any or all of its
non-clustered indexes in different filegroups. If the table has multiple
non-clustered indexes, you can create them in different filegroups.

The leaf level of the non-clustered
index contains pointers to the data pages of the table, either in a heap or a
clustered index. By placing the index on a different filegroup, you have more
detailed control over disk usage and object placement, and potentially better
performance for queries based on the index due to lower resource conflict
between the table and the index.

Partitioned table and its
partitioned indexes are the exception to this rule. You can spread the
partitions for a partitioned table across multiple filegroups, which spreads
the partitioned indexes for those partitions across the same filegroups.

increases when you place the table and its indexes on different physical hard
drives. Performance also increases more when searching the table and indexes on
different hard drives connected to different disk controllers, minimizing
conflicts between read and write requests to disk. This is a scalable design,
which makes it possible to support the data requirements of large enterprise
data applications.

For detail about index placement and
how to place them on different filegroup, see extensive set of documentation at
the Microsoft " Placing
Indexes on Filegroups
” web page.


When you
create or rebuild indexes, you can use padding indexes to affect the space left
in the pages making up the index. A padding
space for users to enter data without having to modify the index’s physical
structure. You determine if you need to support padding when you create or
recreate the index. The data in the index nodes doesn’t change for a read-only
table or stable look-up table, so there is no reason to pad indexes created on
those tables. The situation is different in an OLTP database or with an active,
dynamic table.

When the
database engine needs to add a row to a full page, the database engine has to
split that page to make additional room and keep the index hierarchy intact. Splitting
pages is resource intensive. Depending on index size and other activity in the database,
the process can result in a noticeable performance loss. To prevent splits, or
at least reduce the need for them, you pad the index. When you pad an index,
you specify a fill factor value, which
specifies how much space to leave available. The fill factor can be set to 0,
or to a percentage between 1 and 100. The effect of fill factor values is
described in the following table.


Leaf pages are filled


At least one slot left
open on nonleaf pages (default setting)


To the specified
percentage and nonleaf node pages leave at least one slot open, unless the
index is padded, and then the percentage applies to both leaf and nonleaf


At least one slot left
open on nonleaf pages

When you pad
the index, you leave space open throughout the index hierarchy.  If you do not
pad the index, SQL Server leaves room only in the leaf node pages. In an active
table where data change frequently, you must fill the index and specify a low
fill factor. If you rarely modify the table, you should specify a higher fill
factor or 0. Operationally, a fill factor of 0 works the same as a fill factor
of 100. The padding is applied only when creating or rebuilding the index. The
padding is not maintained during normal operations.

Continue to Part-5:  

In part-5, you will learn about
index operations, index related dynamic management views and functions, and
methods to defragment and indexes.


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